Using Deep Neural Networks and Image Processing to Identify and Categorize Dental Issues in Panoramic X-Ray Images

In recent years, X-rays and other forms of medical imaging have aided dentists in the diagnosis of a wide range of oral health problems. The buccal cavity and other hard-to-reach locations are two places where dental professionals might benefit from the study of panoramic dental x-rays to spot potential issues. The diagnosis may change, however, because of poor image quality or weariness, which might have an impact on the therapy. To aid dentists in establishing accurate diagnoses, this study introduces a unique approach to automated tooth recognition and dental condition categorization utilizing U-Net Models and real-time panoramic X-Ray pictures. The researchers in this study gathered panoramic radiographs from dental offices and analysed them to identify 20 distinct tooth problems. A convolutional neural network (CNN) was trained to acquire semantic segmentation data using the annotated data. In addition, procedures for the processing of images such as semantic segmentation, picture pretreatment, and increased bounding boxes matching teeth detections were carried out